The present application relates to industrial assets and more particularly to systems and/or techniques for selecting and/or updating models configured to diagnose past, present, or expected future health conditions of industrial assets and/or causes of those conditions. The systems and/or techniques find particular application to industrial assets of a power system, such as equipment of a generation sub-system, transmission sub-system, and/or distribution sub-system. However, the systems and/or techniques may also find applicability in non-power related industries where it may be useful to analyze data pertaining to an industrial asset to generate a health profile that describes past, present, or expected future conditions of the industrial asset and/or likely cause(s) of the conditions.
A power system comprises a fleet of industrial assets comprising electrical equipment and non-electrical equipment used to generate, supply, transmit, and/or consume or convert electrical power. Industrial assets of such a power system are usually designed to last decades and perform a critical role in supplying power to consumers. Accordingly, a substantial amount of resources (e.g., time, money, staffing, etc.) are typically dedicated to maintenance planning and early detection of possible failures.
Often, a maintenance schedule is initially devised for an industrial asset based upon a manufacturer's recommended maintenance schedule, and this maintenance schedule may be revised according to events (e.g., usage and/or performance of the industrial asset, trouble reports, outage reports, and/or inspections performed on the industrial asset). By way of example, a yearly inspection may be performed on a distribution sub-station to identify early signs of fatigue, excessive wear, and/or reduced performance. As another example, data may be collected from sensors associated with the industrial asset and analyzed to identify performance changes that may indicate maintenance is needed and/or to identify early indicators of an imminent failure.